This series of NLP (Natural Language Processing) tutorials layout follows a logical flow, covering both fundamentals and advanced concepts.

Each section includes key topics to cover in the tutorials, along with subtopics to ensure comprehensive coverage.

Natural Language Processing (NLP) – Complete Tutorial Series

Goal: Provide a structured, in-depth guide to NLP, covering theoretical foundations, algorithms, and real-world applications.

1. Introduction to NLP & Basic Text Processing

  • Text Preprocessing Techniques
  • Regular Expressions for Text Processing
    • Pattern matching in NLP
    • Applications of regex in NLP tasks
  • Working with Text in Python (NLTK, SpaCy)
    • Loading and processing text data
    • POS tagging basics

2 Spelling Correction & Language Modeling

  • Introduction to Spelling Correction
    • Types of spelling errors (Typographical, Cognitive)
    • Edit distance & Levenshtein distance
    • Noisy Channel Model for spelling correction
  • Introduction to Language Models
    • Definition and importance
    • N-gram Language Models (Unigram, Bigram, Trigram)
    • Probability estimation in Language Models
    • Perplexity and Evaluation of Language Models
  • Implementing Language Models in Python
    • Using NLTK and Scikit-learn

3 Advanced Smoothing Techniques & POS Tagging

  • Smoothing Techniques for Language Models
    • Need for smoothing
    • Laplace Smoothing
    • Add-k Smoothing
    • Good-Turing Smoothing
    • Back-off and Interpolation
  • Part-of-Speech (POS) Tagging
    • Definition and importance
    • POS tagging datasets (Penn Treebank)
    • Rule-based vs. Statistical POS tagging
    • Hidden Markov Models (HMM) for POS tagging
    • Maximum Entropy Models
  • POS Tagging in Python
    • NLTK and SpaCy-based implementations

4 Sequential Tagging Models – MaxEnt, CRF

  • Introduction to Sequential Tagging
    • Named Entity Recognition (NER)
    • Chunking and Shallow Parsing
  • Maximum Entropy (MaxEnt) Model
    • Understanding Maximum Entropy for NLP tasks
    • Training and implementing MaxEnt models
  • Conditional Random Fields (CRF)
    • Basics of CRF and sequence labeling
    • CRF vs. HMMs for sequence modeling
    • Implementing CRF in Python (CRFsuite, sklearn-crfsuite)

5 Syntax – Constituency Parsing

  • Introduction to Parsing in NLP
    • Syntactic Analysis and its role in NLP
    • Constituency vs. Dependency Parsing
  • Constituency Parsing
    • Context-Free Grammar (CFG)
    • Probabilistic Context-Free Grammars (PCFG)
    • CKY Algorithm for parsing
  • Implementing Constituency Parsing
    • Stanford NLP, NLTK-based approaches

6 Dependency Parsing

  • Introduction to Dependency Parsing
    • How dependency parsing differs from constituency parsing
    • Applications in NLP (Syntax-based translation, Coreference Resolution)
  • Dependency Parsing Algorithms
    • Transition-based parsing
    • Graph-based parsing (Eisner Algorithm, MST Parser)
  • Dependency Parsing with SpaCy & Stanford NLP
    • Implementing dependency parsing in Python

7 Distributional Semantics

  • Meaning Representation in NLP
    • Traditional vs. Distributional approaches
    • Vector Space Models (VSM)
  • Word Embeddings
    • Word2Vec (CBOW & Skip-gram)
    • GloVe
    • FastText
  • Contextual Embeddings
    • Introduction to BERT, ELMo, and Transformer-based embeddings
  • Implementing Word Embeddings in Python
    • Using Gensim and Hugging Face Transformers

8 Lexical Semantics

  • Lexical Semantics Overview
    • Meaning of words and their relationships
    • WordNet and Thesaurus-based methods
  • Word Sense Disambiguation (WSD)
    • Lesk Algorithm
    • Supervised vs. Unsupervised WSD techniques
  • Distributional Semantics for Lexical Semantics
    • Measuring semantic similarity
    • Cosine similarity, Jaccard similarity
  • Python Implementations for WSD & WordNet
    • Using NLTK’s WordNet API

9 Topic Modeling

  • Introduction to Topic Modeling
    • Definition and real-world applications
  • Latent Dirichlet Allocation (LDA)
    • How LDA works
    • Gibbs Sampling and Topic Assignments
  • Non-negative Matrix Factorization (NMF)
    • NMF vs. LDA for topic modeling
  • Implementing Topic Modeling in Python
    • Using Gensim and Scikit-learn

10 Entity Linking & Information Extraction

  • Named Entity Recognition (NER)
    • Rule-based vs. Statistical methods
    • Pre-trained models (SpaCy, BERT-based NER)
  • Coreference Resolution
    • Anaphora and Pronoun Resolution
    • Rule-based and Machine Learning approaches
  • Entity Linking (EL)
    • Linking named entities to knowledge bases (e.g., Wikipedia, Wikidata)
  • Relation Extraction
    • Supervised and Unsupervised approaches

11 Text Summarization & Text Classification

  • Text Summarization Techniques
    • Extractive vs. Abstractive Summarization
    • TextRank Algorithm
    • Transformer-based Summarization
  • Text Classification
    • Supervised Learning for Text Classification
    • Naïve Bayes, SVM, Neural Networks
    • Transformer models (BERT, RoBERTa)
  • Text Classification in Python
    • Using Scikit-learn and Hugging Face Transformers

12 Sentiment Analysis & Opinion Mining

  • Understanding Sentiment Analysis
    • Importance of sentiment analysis in business and social media
  • Sentiment Analysis Approaches
    • Lexicon-based methods (VADER, TextBlob)
    • Machine Learning-based methods (SVM, CNN, LSTM)
  • Fine-tuning BERT for Sentiment Analysis
    • Using pre-trained models for sentiment classification
  • Implementing Sentiment Analysis in Python
    • Using Scikit-learn, NLTK, and Hugging Face

Error Handling in Python

Conclusion & Next Steps

  • Recap of key NLP techniques
  • Challenges in NLP & Future Trends
    • Large Language Models (LLMs)
    • Explainable AI in NLP
    • NLP applications in industry
  • Next Steps for Learners
    • Recommended Books & Research Papers
    • Open-source NLP Projects to Contribute